import logging
from collections.abc import Callable
from importlib import util
from pathlib import Path
if util.find_spec("pandas"):
import pandas as pd
pandas_installed = True
else: # coverage: ignore
pandas_installed = False
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision.datasets.utils import (
check_integrity,
download_and_extract_archive,
download_url,
)
boston_column_names = [
"CRIM",
"ZN",
"INDUS",
"CHAS",
"NOX",
"RM",
"AGE",
"DIS",
"RAD",
"TAX",
"PTRATIO",
"B",
"LSTAT",
"MEDV",
]
energy_prediction_column_names = [
"Appliances",
"lights",
"T1",
"RH_1",
"T2",
"RH_2",
"T3",
"RH_3",
"T4",
"RH_4",
"T5",
"RH_5",
"T6",
"RH_6",
"T7",
"RH_7",
"T8",
"RH_8",
"T9",
"RH_9",
"T_out", # Dropped
]
[docs]class UCIRegression(Dataset):
"""The UCI regression datasets.
Args:
root (str): Root directory of the datasets.
train (bool, optional): If True, creates dataset from training set,
otherwise creates from test set.
transform (callable, optional): A function/transform that takes in a
numpy array and returns a transformed version.
target_transform (callable, optional): A function/transform that takes
in the target and transforms it.
dataset_name (str, optional): The name of the dataset. One of
"boston-housing", "concrete", "energy", "kin8nm",
"naval-propulsion-plant", "power-plant", "protein",
"wine-quality-red", and "yacht".
download (bool, optional): If true, downloads the dataset from the
internet and puts it in root directory. If dataset is already
downloaded, it is not downloaded again.
Note - Ethics:
You may want to avoid using the boston-housing dataset because of
ethical concerns.
Note - License:
The licenses of the datasets may differ from TorchUncertainty's
license. Check before use.
"""
root_appendix = "uci_regression"
uci_subsets = [
"boston",
"concrete",
"energy-efficiency",
"energy-prediction",
"kin8nm",
"naval-propulsion-plant",
"power-plant",
"protein",
"wine-quality-red",
"yacht",
]
md5_tgz = [
"d4accdce7a25600298819f8e28e8d593",
"eba3e28907d4515244165b6b2c311b7b",
"2018fb7b50778fdc1304d50a78874579",
"d0f0f8ceaaf45df2233ce0600097bd84",
"df08c665b7665809e74e32b107836a3a",
"54f4febcf51bdba12e1ca63e28b3e973",
"f5065a616eae05eb4ecae445ecf6e720",
"37bcb77a8abad274a987439e6a3de632",
"0ddfa7a9379510fe7ff88b9930e3c332",
"4e6727f462779e2d396e8f7d2ddb79a3",
]
urls = [
"https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data",
"https://archive.ics.uci.edu/static/public/165/concrete+compressive+strength.zip",
"https://archive.ics.uci.edu/static/public/242/energy+efficiency.zip",
"https://archive.ics.uci.edu/static/public/374/appliances+energy+prediction.zip",
"https://zenodo.org/records/14645866/files/kin8nm.csv",
"https://raw.githubusercontent.com/luishpinto/cm-naval-propulsion-plant/master/data.csv",
"https://archive.ics.uci.edu/static/public/294/combined+cycle+power+plant.zip",
"https://archive.ics.uci.edu/static/public/265/physicochemical+"
"properties+of+protein+tertiary+structure.zip",
"https://archive.ics.uci.edu/static/public/186/wine+quality.zip",
"https://archive.ics.uci.edu/static/public/243/yacht+hydrodynamics.zip",
]
def __init__(
self,
root: Path | str,
transform: Callable | None = None,
target_transform: Callable | None = None,
dataset_name: str = "energy",
download: bool = False,
seed: int = 42,
shuffle: bool = True,
) -> None:
super().__init__()
self.root = Path(root)
self.transform = transform
self.target_transform = target_transform
self.seed = seed
self.shuffle = shuffle
if dataset_name not in self.uci_subsets:
raise ValueError(
f"The dataset {dataset_name} is not implemented. "
"`dataset_name` should be one of {self.uci_subsets}."
)
self.dataset_name = dataset_name
dataset_id = self.uci_subsets.index(dataset_name)
self.url = self.urls[dataset_id]
self.start_filename = self.url.split("/")[-1]
self.md5 = self.md5_tgz[dataset_id]
if download:
self.download()
self._make_dataset()
def __len__(self) -> int:
"""Get the length of the dataset."""
return self.data.shape[0]
def _check_integrity(self) -> bool:
"""Check the integrity of the dataset(s)."""
return check_integrity(
self.root / self.root_appendix / Path(self.start_filename),
self.md5,
)
def _standardize(self) -> None:
self.data = (self.data - self.data_mean) / self.data_std
self.targets = (self.targets - self.target_mean) / self.target_std
def _compute_statistics(self) -> None:
self.data_mean = self.data.mean(axis=0)
self.data_std = self.data.std(axis=0)
self.data_std[self.data_std == 0] = 1
self.target_mean = self.targets.mean(axis=0)
self.target_std = self.targets.std(axis=0)
[docs] def download(self) -> None:
"""Download and extract dataset."""
if self._check_integrity():
logging.info("Files already downloaded and verified")
return
if self.url is None:
raise ValueError(f"The dataset {self.dataset_name} is not available for download.")
download_root = self.root / self.root_appendix / self.dataset_name
if self.dataset_name == "boston":
download_url(
self.url,
root=download_root,
filename="housing.data",
)
elif self.dataset_name == "kin8nm":
download_url(
self.url,
root=download_root,
filename="kin8nm.csv",
)
elif self.dataset_name == "naval-propulsion-plant":
download_url(
self.url,
root=download_root,
filename="data.csv",
)
else:
download_and_extract_archive(
self.url,
download_root=download_root,
extract_root=download_root,
filename=self.start_filename,
md5=self.md5,
)
def _make_dataset(self) -> None:
"""Create dataset from extracted files."""
if not pandas_installed: # coverage: ignore
raise ImportError(
"Please install torch_uncertainty with the tabular option:"
"""pip install -U "torch_uncertainty[tabular]"."""
)
path = self.root / self.root_appendix / self.dataset_name
if self.dataset_name == "boston":
array = pd.read_table(
path / "housing.data",
names=boston_column_names,
header=None,
delim_whitespace=True,
)
elif self.dataset_name == "concrete":
array = pd.read_excel(path / "Concrete_Data.xls").to_numpy()
elif self.dataset_name == "energy-efficiency":
array = pd.read_excel(path / "ENB2012_data.xlsx").to_numpy()
elif self.dataset_name == "energy-prediction":
array = pd.read_csv(path / "energydata_complete.csv")[
energy_prediction_column_names
].to_numpy()
elif self.dataset_name == "kin8nm":
array = pd.read_csv(path / "kin8nm.csv").to_numpy()
elif self.dataset_name == "naval-propulsion-plant":
df = pd.read_csv(path / "data.csv", header=None, sep=";", decimal=",")
# convert Ex to 10^x and remove second target
array = df.apply(pd.to_numeric, errors="coerce").to_numpy()[:, :-1]
elif self.dataset_name == "protein":
array = pd.read_csv(
path / "CASP.csv",
).to_numpy()
elif self.dataset_name == "wine-quality-red":
array = pd.read_csv(
path / "winequality-red.csv",
sep=";",
).to_numpy()
elif self.dataset_name == "yacht":
array = pd.read_csv(
path / "yacht_hydrodynamics.data",
delim_whitespace=True,
header=None,
).to_numpy()
else:
raise ValueError("Dataset not implemented.")
array = torch.as_tensor(array).float()
if self.dataset_name == "energy-efficiency":
self.data = array[:, 2:-3]
self.targets = array[:, -2]
else:
self.data = array[:, :-1]
self.targets = array[:, -1]
self._compute_statistics()
self._standardize()
if self.dataset_name == "energy-prediction":
self.data = F.pad(self.data, (0, 0, 13, 0), value=0)
if self.shuffle:
gen = torch.Generator()
gen.manual_seed(self.seed)
indexes = torch.randperm(array.shape[0], generator=gen)
array = array[indexes]
def __getitem__(self, index: int) -> tuple[torch.Tensor, torch.Tensor]:
"""Get sample and target for a given index."""
if self.dataset_name == "energy-prediction":
data = self.data[index : index + 13, :]
target = self.data[index : index + 13, :]
return data, target
data = self.data[index]
if self.transform is not None:
data = self.transform(data)
target = self.targets[index]
if self.target_transform is not None:
target = self.target_transform(target)
return data, target
